Work

Towards Dynamic Energy/Carbon Trading and Resource Allocation for MEC: A Two-Timescale Deep Reinforcement Learning Approach

Year: 2025

Type: conference-paper

Abstract: Integrating smart grid and carbon market into mobile edge computing (MEC) systems presents significant potential for reducing both operational energy costs and carbon footprints. This paper proposes a joint optimization framework for energy/carbon trading and resource allocation in MEC systems participating in grid-energy and carbon markets, aiming to minimize the long-term time-averaged cost of the energy/carbon tradings and the energy consumption of the system. Built on a twotimescale multi-agent deep reinforcement learning (TTMADRL) optimization framework, the Deep Deterministic Policy Gradient (DDPG) is generalized to make decisions on energy and carbon transactions at the large timescale; while at the small timescale, the task offloading schedules and CPU frequencies are distributively determined at each device by using the Multi-Agent DDPG (MADDPG) algorithm with enhanced scalability. Simulations demonstrate that the proposed TTMADRL achieves a $\mathbf{7 5. 4 4 \%}$ reduction in system costs compared to baseline approaches.

Source: IEEE/CIC International Conference on Communications in China (ICCC)

DOI: 10.1109/iccc65529.2025.11148917

Authors: Xiaojing Chen, Yijun Ding, Wei Ni, Xin Wang, Yichuang Sun, Shunqing Zhang

Institutions: Shanghai University, Commonwealth Scientific and Industrial Research Organisation, Fudan University, University of Hertfordshire

Language: English

Topic: IoT and Edge/Fog Computing

Subfield: Computer Networks and Communications

Field: Computer Science

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Climate action, Affordable and clean energy

Open Access status: green

OpenAlex ID: W4414117190